Search results for: finite state machine

Authors: Shraddha Jain Sharma, Ratnalata Gupta

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Predicting stock movements in the financial market is both difficult and rewarding. Analysts and academics are increasingly using advanced approaches such as machine learning techniques to anticipate stock price patterns, thanks to the expanding capacity of computing and the recent advent of graphics processing units and tensor processing units. Stock market prediction is a type of time series prediction that is incredibly difficult to do since stock prices are influenced by a variety of financial, socioeconomic, and political factors. Furthermore, even minor mistakes in stock market price forecasts can result in significant losses for companies that employ the findings of stock market price prediction for financial analysis and investment. Soft computing techniques are increasingly being employed for stock market prediction due to their better accuracy than traditional statistical methodologies. The proposed research looks at the need for soft computing techniques in stock market prediction, the numerous soft computing approaches that are important to the field, past work in the area with their prominent features, and the significant problems or issue domain that the area involves. For constructing a predictive model, the major focus is on neural networks and fuzzy logic. The stock market is extremely unpredictable, and it is unquestionably tough to correctly predict based on certain characteristics. This study provides a complete overview of the numerous strategies investigated for high accuracy prediction, with a focus on the most important characteristics.

Keywords: stock market prediction, artificial intelligence, artificial neural networks, fuzzy logic, accuracy, deep learning, machine learning, stock price, trading volume

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Authors: Joel Mintz, Kyle Huntley, Waseem Wahood, Samuel Raine, Farzanna Haffizulla

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The effect of different state government interventions on COVID-19 health outcomes is currently unknown. Stay at home (SAH) orders, all non-essential business closures and school closures in the Northeastern US were examined. A linear correlation between the peak number of new daily COVID-19 positive tests, hospitalizations and deaths per capita and the elapsed time between government issued guidance and a fixed number of COVID-19 deaths in each state was performed. Earlier government interventions were correlated with lower peak healthcare burden. Statewide closures of schools and non-essential businesses showed significantly greater (p<.001) correlation to peak COVID-19 disease burden as compared to a statewide SAH. The implications of these findings require further study to determine the effectiveness of these interventions.

Keywords: Coronavirus, epidemiology, government intervention, public health, social distancing

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Authors: Abdullah Bal

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This paper presents a one-class classification (OCC) technique based on Fukunaga-Koontz Transform (FKT) for binary classification problems. The FKT is originally a powerful tool to feature selection and ordering for two-class problems. To utilize the standard FKT for data domain description problem (i.e., one-class classification), in this paper, a set of non-class samples which exist outside of positive class (target class) describing boundary formed with limited training data has been constructed synthetically. The tunnel-like decision boundary around upper and lower border of target class samples has been designed using statistical properties of feature vectors belonging to the training data. To capture higher order of statistics of data and increase discrimination ability, the proposed method, termed one-class FKT (OC-FKT), has been extended to its nonlinear version via kernel machines and referred as OC-KFKT for short. Multiple kernel learning (MKL) is a favorable family of machine learning such that tries to find an optimal combination of a set of sub-kernels to achieve a better result. However, the discriminative ability of some of the base kernels may be low and the OC-KFKT designed by this type of kernels leads to unsatisfactory classification performance. To address this problem, the quality of sub-kernels should be evaluated, and the weak kernels must be discarded before the final decision making process. MKL/OC-FKT and selective MKL/OC-FKT frameworks have been designed stimulated by ensemble learning (EL) to weight and then select the sub-classifiers using the discriminability and diversities measured by eigenvalue ratios. The eigenvalue ratios have been assessed based on their regions on the FKT subspaces. The comparative experiments, performed on various low and high dimensional data, against state-of-the-art algorithms confirm the effectiveness of our techniques, especially in case of small sample size (SSS) conditions.

Keywords: ensemble methods, fukunaga-koontz transform, kernel-based methods, multiple kernel learning, one-class classification

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Authors: Valentina A. Mikhailova, Serguei V. Feskov, Anatoly I. Ivanov

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In the last decade the nonequilibrium charge transfer have attracted considerable interest from the scientific community. Examples of such processes are the charge recombination in excited donor-acceptor complexes and the intramolecular electron transfer from the second excited electronic state. In these reactions the charge transfer proceeds predominantly in the nonequilibrium mode. In the excited donor-acceptor complexes the nuclear nonequilibrium is created by the pump pulse. The intramolecular electron transfer from the second excited electronic state is an example where the nuclear nonequilibrium is created by the forward electron transfer. The kinetics of these nonequilibrium reactions demonstrate a number of peculiar properties. Most important from them are: (i) the absence of the Marcus normal region in the free energy gap law for the charge recombination in excited donor-acceptor complexes, (ii) extremely low quantum yield of thermalized charge separated state in the ultrafast charge transfer from the second excited state, (iii) the nonexponential charge recombination dynamics in excited donor-acceptor complexes, (iv) the dependence of the charge transfer rate constant on the excitation pulse frequency. This report shows that most of these kinetic features can be well reproduced in the framework of stochastic point-transition multichannel model. The model involves an explicit description of the nonequilibrium excited state formation by the pump pulse and accounts for the reorganization of intramolecular high-frequency vibrational modes, for their relaxation as well as for the solvent relaxation. The model is able to quantitatively reproduce complex nonequilibrium charge transfer kinetics observed in modern experiments. The interpretation of the nonequilibrium effects from a unified point of view in the terms of the multichannel point transition stochastic model allows to see similarities and differences of electron transfer mechanism in various molecular donor-acceptor systems and formulates general regularities inherent in these phenomena. The nonequilibrium effects in photoinduced ultrafast charge transfer which have been studied for the last 10 years are analyzed. The methods of suppression of the ultrafast charge recombination, similarities and dissimilarities of electron transfer mechanism in different molecular donor-acceptor systems are discussed. The extremely low quantum yield of the thermalized charge separated state observed in the ultrafast charge transfer from the second excited state in the complex consisting of 1,2,4-trimethoxybenzene and tetracyanoethylene in acetonitrile solution directly demonstrates that its effectiveness can be close to unity. This experimental finding supports the idea that the nonequilibrium charge recombination in the excited donor-acceptor complexes can be also very effective so that the part of thermalized complexes is negligible. It is discussed the regularities inherent to the equilibrium and nonequilibrium reactions. Their fundamental differences are analyzed. Namely the opposite dependencies of the charge transfer rates on the dynamical properties of the solvent. The increase of the solvent viscosity results in decreasing the thermal rate and vice versa increasing the nonequilibrium rate. The dependencies of the rates on the solvent reorganization energy and the free energy gap also can considerably differ. This work was supported by the Russian Science Foundation (Grant No. 16-13-10122).

Keywords: Charge recombination, higher excited states, free energy gap law, nonequilibrium

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Authors: Shahid Minhas

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This study 'Dissecting the Hindu and New York Times perspective on War on Terror' conducted to determine the comparative perspective and portrayal of war on Terror in Pakistan. The study also aimed to find out how these two selected countries i.e. India and America, press depict and how considerably they cover the war on terror in Pakistan, which also benefits to know the policy of government concerned. This study also analyzes to what extent Indian and American press followed its foreign policy guidelines in the coverage of the war on Terror in Pakistan. This study observes that actually, the New York Times pays little consideration to Pakistan and even the third words countries, and when it pays consideration, it tends to cover negative news. Pakistan is more frequently covers unfavorable than positive likewise encircled more frequently as a fundamentalist than a liberal state by the Hindu, While stance that Pakistan has played the anti-India tag to develop equities cultivate non-state groups as chattels.

Keywords: war on terror, terrorism, Pak-India relation, Pak-US relation

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Authors: Chandu Rathnayake, Isuri Anuradha

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Early detection and accurate diagnosis of lung diseases play a crucial role in improving patient prognosis. However, conventional diagnostic methods heavily rely on subjective symptom assessments and medical imaging, often causing delays in diagnosis and treatment. To overcome this challenge, we propose a novel lung disease prediction system that integrates patient symptoms and X-ray images to provide a comprehensive and reliable diagnosis.In this project, develop a mobile application specifically designed for detecting lung diseases. Our application leverages both patient symptoms and X-ray images to facilitate diagnosis. By combining these two sources of information, our application delivers a more accurate and comprehensive assessment of the patient's condition, minimizing the risk of misdiagnosis. Our primary aim is to create a user-friendly and accessible tool, particularly important given the current circumstances where many patients face limitations in visiting healthcare facilities. To achieve this, we employ several state-of-the-art algorithms. Firstly, the Decision Tree algorithm is utilized for efficient symptom-based classification. It analyzes patient symptoms and creates a tree-like model to predict the presence of specific lung diseases. Secondly, we employ the Random Forest algorithm, which enhances predictive power by aggregating multiple decision trees. This ensemble technique improves the accuracy and robustness of the diagnosis. Furthermore, we incorporate a deep learning model using Convolutional Neural Network (CNN) with the RestNet50 pre-trained model. CNNs are well-suited for image analysis and feature extraction. By training CNN on a large dataset of X-ray images, it learns to identify patterns and features indicative of lung diseases. The RestNet50 architecture, known for its excellent performance in image recognition tasks, enhances the efficiency and accuracy of our deep learning model. By combining the outputs of the decision tree-based algorithms and the deep learning model, our mobile application generates a comprehensive lung disease prediction. The application provides users with an intuitive interface to input their symptoms and upload X-ray images for analysis. The prediction generated by the system offers valuable insights into the likelihood of various lung diseases, enabling individuals to take appropriate actions and seek timely medical attention. Our proposed mobile application has significant potential to address the rising prevalence of lung diseases, particularly among young individuals with smoking addictions. By providing a quick and user-friendly approach to assessing lung health, our application empowers individuals to monitor their well-being conveniently. This solution also offers immense value in the context of limited access to healthcare facilities, enabling timely detection and intervention. In conclusion, our research presents a comprehensive lung disease prediction system that combines patient symptoms and X-ray images using advanced algorithms. By developing a mobile application, we provide an accessible tool for individuals to assess their lung health conveniently. This solution has the potential to make a significant impact on the early detection and management of lung diseases, benefiting both patients and healthcare providers.

Keywords: CNN, random forest, decision tree, machine learning, deep learning

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Authors: H. Golchoobian, S. Saedodin, M. H. Taheri, A. Sarafraz

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In this paper, natural convection in an attic is numerically investigated. The geometry of the problem is considered to be a triangular enclosure. ANSYS Fluent software is used for modeling and numerical solution. This study is for steady state. Four right-angled triangles with height to base ratios of 2, 1, 0.5 and 0.25 are considered. The behavior of various parameters related to its performance, including temperature distribution and velocity vectors are evaluated, and graphs for the Nusselt number have been drawn. Also, in this study, the effect of geometric shape of enclosure with different height-to-base ratios has been evaluated for three types of boundary conditions of winter, summer day and one another state. It can be concluded that as the bottom side temperature and ratio of base to height of the enclosure increases, the convective effects become more prominent and circulation happened.

Keywords: enclosure, natural convection, numerical solution, Nusselt number, triangular

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Authors: Prasanta Saini, Junaid Jibran Jawed, Subrata Majumdar

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Infections caused by soil-transmitted helminths (STH) are the major problem in all the nations of the world. The major focus of STH research is to study the prevalence of three major helminths, such as Ascaris, Trituris and hookworm. Here we are reporting the prevalence and intensity of the STH in the school children of the state of Uttar Pradesh, India. The aim of the study is to assess the prevalence and risk factors of STH infection among the school children, aged between 5-10 years in 27 districts randomly selected districts with covering nine agro-climatic zones of Uttar Pradesh, India. For this cross-sectional survey, we have selected the populations of government primary school going children in Uttar Pradesh. The sampling was performed in the nine different agro-climatic zones. Every individual of the study populations filled their daily information in the questioner's form and then the sample was collected and processed by kato-katz methods by following the guidelines of WHO. In this method, the sampling was performed in total of 6421 populations. A total of 6,421 children from 130 schools were surveyed. Infection with any soil-transmitted helminths was detected among 4,578 children with an overall prevalence of 75.6% (95% CI: 65.3-83.6). Among the 6421 population, the prevalence of Ascaris is 69.6% (95% CL 57.97-79.11), hookworm is 22.7% (95%CL 19.3-26.3) and Trichuris sp is 4.6% (95% CL 0.8-21.6), so the predicted prevalence map indicates that the STH infection was hyperendemic in this state. The findings of our survey in 130 schools covering 9 agro-climatic with one or more soil transmitted helminths. Majority of STH infections were of light intensity. STH infection was hyper-endemic in entire state, except three zones in western Uttar Pradesh. High prevalence ( > 75%) in all age groups also indicate little impact of existing deworming initiatives, including those among pre-school aged children. WHO recommends annual treatment in areas where STH prevalence is between 20% and 50%, and, a bi-annual treatment in areas with prevalence rates of over 50%. In view of high prevalence of STH infection in Uttar Pradesh, it is strongly recommended to initiate a deworming programme for school children in the state. Although our survey was among primary school children, high prevalence among children aged 4-6 years also indicates the need to strengthen the existing deworming programs for pre-school children. Extending the benefits of deworming to pre-school children through deworming in Anganwadi schools would further reduce to decrease the load of infection in community. As a long-term solution for control STH infection, it is also necessary to improve the sanitation levels in the area, as majority of the houses did not have latrines and most of the children were defecating in open fields, a factor that was found to be significantly associated with STH infection.

Keywords: prevalence, school going children, soil transmitted helminthes, Uttar Pradesh-India

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Authors: Grace Onubedo

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The primary objective of this research study is to contribute to and deepen the understanding of the realities and conditions for which children recruited by violent extremist organisations in Nigeria live, as well as ascertain the state of their mental health following their reunification with either family or protection workers. The research is intended to contribute to a more focused child protection programming agenda for children associated with armed forces and groups in Nigeria and the wider conflict setting. The extent to which violence has affected the psychological well-being and mental health of children abducted and exposed to activities of Violent Extremist groups remains a largely empirical question. This research attempts to answer the following research questions with the aim of providing further evidences for informed programming: I. What are the demographic characteristics of children associated with armed groups? II. What is the state of their mental health? III. What is the relationship between their background and their mental health?

Keywords: counterterrorism, psychosocial support, psychological distress, children, armed groups

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Authors: Kheira Hamaida, Mohamed Salah Halati

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In this current paper, our aim work is to link as possible the obtained simulation results and the other experimental ones, just focusing on the electronic and optical properties of ZnSe. The predictive spectra of the total and partial densities of states using the Full Potential Linearized/Augmented Plane Wave method with the newly Tran-Blaha (TB) modified Becke-Johnson (mBJ) exchange-correlation potential (EXC). So the upper valence energy (UVE) levels contain the relative contribution of Se-(4p and 3d) states with considerable contribution from the electrons of Zn-2s orbital. The dielectric function of w-ZnSe, with its two parts, appears with a noticeable anisotropy character. The microscopic origins of the electronic states that are responsible for the observed peaks in the spectrum are determined through the decomposition of the spectrum to the individual contributions of the electronic transitions between the pairs of bands, where Vi is an occupied state in the valence band, and Ci is an unoccupied state in the conduction band. X-PES (X Ray-Photo Electron Spectroscopy) is an important technique used to probe the homogeneity, stoichiometry, and purity state of the title compound. In order to check the electron transitions derived from simulations and the others from Reflected Electron Energy Loss Spectroscopy (REELS) technique which was of great sensitivity, is used to determine the interband electronic transitions. In the optical window (Eg), all the electron energy states created were also determined through the specific gaussian deconvolution of the photoluminescence spectrum (PLS) that probed under a room temperature (RT).

Keywords: spectroscopy, WIEN2K, IIB-VIA semiconductors, dielectric function

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Authors: Justine Cyril E. Nunag, Nestor B. Sabado Jr., Jienne Chester M. Tolosa

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Concrete has high compressive strength but a low-tension strength. The small tensile strength of concrete is regarded as its primary weakness, which is why it is typically reinforced with steel, a material that is resistant to tension. Even with steel, however, cracking can occur. In strengthening concrete, only a few researchers have modified the water to be used in a concrete mix. This study aims to compare the split tensile strength of normal structural concrete to concrete prepared with magnetic water and a quick setting admixture. In this context, magnetic water is defined as tap water that has undergone a magnetic process to become magnetized water. To test the hypothesis that magnetized concrete leads to higher split tensile strength, twenty concrete specimens were made. There were five groups, each with five samples, that were differentiated by the number of cycles (0, 50, 100, and 150). The data from the Universal Testing Machine's split tensile strength were then analyzed using various statistical models and tests to determine the significant effect of magnetized water. The result showed a moderate (+0.579) but still significant degree of correlation. The researchers also discovered that using magnetic water for 50 cycles did not result in a significant increase in the concrete's split tensile strength, which influenced the analysis of variance. These results suggest that a concrete mix containing magnetic water and a quick-setting admixture alters the typical split tensile strength of normal concrete. Magnetic water has a significant impact on concrete tensile strength. The hardness property of magnetic water influenced the split tensile strength of concrete. In addition, a higher number of cycles results in a strong water magnetism. The laboratory test results show that a higher cycle translates to a higher tensile strength.

Keywords: hardness property, magnetic water, quick-setting admixture, split tensile strength, universal testing machine

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Authors: Viktória Sugár, Attila Talamon, András Horkai, Michihiro Kita

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The dense urban fabric of the 7^th district of Budapest -known as the former Jewish Quarter-, contains mainly historical style, multi-story tenement houses with courtyards. The high population density and the unsatisfactory energetic state of the buildings result high energy consumption. As a preliminary survey of a complex rehabilitation plan, the authors aim to determine the energy demand of the area. The energy demand was calculated by analyzing the structure and the energy consumption of each building by using Geographic Information System (GIS) methods. The carbon dioxide emission was also calculated, to assess the potential of reducing the present state value by complex structural and energetic rehabilitation. As a main focus of the survey, an energy intensity map has been created about the area.

Keywords: CO₂, energy intensity map, geographic information system (GIS), Hungary, Jewish quarter, rehabilitation

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Authors: Richard L. Summers

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The advanced computational analysis of biological systems is becoming increasingly dependent upon an understanding of the information-theoretic structure of the materials, energy and interactive processes that comprise those systems. The stability and survival of these living systems are fundamentally contingent upon their ability to acquire and process the meaning of information concerning the physical state of its biological continuum (biocontinuum). The drive for adaptive system reconciliation of a divergence from steady-state within this biocontinuum can be described by an information metric-based formulation of the process for actionable knowledge acquisition that incorporates the axiomatic inference of Kullback-Leibler information minimization driven by survival replicator dynamics. If the mathematical expression of this process is the Lagrangian integrand for any change within the biocontinuum then it can also be considered as an action functional for the living system. In the direct method of Lyapunov, such a summarizing mathematical formulation of global system behavior based on the driving forces of energy currents and constraints within the system can serve as a platform for the analysis of stability. As the system evolves in time in response to biocontinuum perturbations, the summarizing function then conveys information about its overall stability. This stability information portends survival and therefore has absolute existential meaning for the living system. The first derivative of the Lyapunov energy information function will have a negative trajectory toward a system's steady state if the driving force is dissipating. By contrast, system instability leading to system dissolution will have a positive trajectory. The direction and magnitude of the vector for the trajectory then serves as a quantifiable signature of the meaning associated with the living system’s stability information, homeostasis and survival potential.

Keywords: meaning, information, Lyapunov, living systems

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Authors: Shingo Murakami, Shinichi Enoki

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In structures, stress concentration is a factor of fatigue fracture. Basically, the stress concentration is a phenomenon that should be avoided. However, it is difficult to avoid the stress concentration. Therefore, relaxation of the stress concentration is important. The stress concentration arises from notches and circular holes. There is a relaxation method that a composite patch covers a notch and a circular hole. This relaxation method is used to repair aerial wings, but it is not systematized. Composites are more expensive than single materials. Accordingly, we propose the relaxation method that a single material patch covers a notch and a circular hole, and aim to systematize this relaxation method. We performed FEA (Finite Element Analysis) about an object by using a three-dimensional FEA model. The object was that a patch adheres to a plate with a circular hole. And, a uniaxial tensile load acts on the patched plate with a circular hole. In the three-dimensional FEA model, it is not easy to model the adhesion layer. Basically, the yield stress of the adhesive is smaller than that of adherents. Accordingly, the adhesion layer gets to plastic deformation earlier than the adherents under the yield load of adherents. Therefore, we propose the three-dimensional FEA model which is applied a nonlinear elastic region to the adhesion layer. The nonlinear elastic region was calculated by a bilinear approximation. We compared the analysis results with the tensile test results to confirm whether the analysis model has usefulness. As a result, the analysis results agreed with the tensile test results. And, we confirmed that the analysis model has usefulness. As a result that the three-dimensional FEA model was used to the analysis, it was confirmed that an out-of-plane deformation occurred to the patched plate with a circular hole. The out-of-plane deformation causes stress increase of the patched plate with a circular hole. Therefore, we investigated that the out-of-plane deformation affects relaxation of the stress concentration in the plate with a circular hole on this relaxation method. As a result, it was confirmed that the out-of-plane deformation inhibits relaxation of the stress concentration on the plate with a circular hole.

Keywords: stress concentration, patch, out-of-plane deformation, Finite Element Analysis

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Authors: Olufunmilayo Folaranmi

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The study aimed at assessing the effectiveness of micro-credit scheme of (COWAD) in enhancing the living standard of women in selected local government areas of Oyo State. A survey research design was adopted for the study. A sample of 250 respondents was purposively selected for the study while a structured questionnaire tagged Effectiveness of Micro-Credit Scheme of Community Women and Development and Living Standards of Women Questionnaire (EMCSCWDQ) was designed to collect data for the study. Data collected was analyzed using frequency distribution, tables, percentages and chi-square statistics. Three hypotheses were tested for the study at 0.05 level of significance. Findings from the study indicated that loan provided by COWAD for women in selected local government areas towards improving their economic conditions has improved the living conditions of the women, promoted their general welfare, and reduced their poverty level. Findings also showed that some beneficiaries were not able to pay back, therefore reducing the effectiveness for future beneficiaries. Based on the findings, it was recommended that the providers of various micro-credit schemes of the state should design a convenient pattern of payment which will provide enough time for the beneficiaries of the loan to sell their goods or work for proper and timely payment. Also, the problem of collateral should be reviewed as the majority of women involved are poor. Other recommendations include replication of COWAD facilities in other NGOs as well as sustainability of the facility.

Keywords: micro-credit scheme, welfare, women, development, poverty

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Authors: Abas Mohsenzadeh, Mina Arya, Kim Bolton

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Catalytic combustion of hydrocarbons is an important technology developed to produce energy with minimum pollutant formation. The catalyst plays a key role in this process which operates at lower temperatures compared to conventional flame combustion. The energetics of the direct combustion of hydrocarbons (CH → C + H) on a series of metal surfaces including Ag, Au, Al, Cu, Rh, Pt, Pd, Ni, Fe and Co were investigated using density functional theory (DFT). Brønsted-Evans-Polanyi (BEP) and transition state scaling (TSS) correlations were proposed based on DFT calculations on the Ag, Au, Al, Cu, Rh, Pt and Pd surfaces. These correlations were then used to estimate the energetics on Fe, Ni and Co surfaces. Results showed that the estimated reaction and activation energies by BEP and TSS correlations on Fe, Ni and Co surfaces are in an excellent agreement with those obtained by DFT calculations. Therefore these correlations can be efficiently used to predict energetics of similar reactions on these surfaces without doing computationally costly transition state calculations. It was found that the activation barrier for CH dissociation follows the order Ag ˃ Au ˃ Al ˃ Cu ˃ Pt ˃ Pd ˃ Ni > Co > Rh > Fe. Also, BEP (with R2 value of 0.96) and TSS correlations (with R2 value of 0.99) support the results.

Keywords: BEP, DFT, hydrocarbon combustion, metal surfaces, TSS

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Authors: Muogbo Chukwudi Peter, Gbabo Agidi

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Influence of different turmeric rhizome sizes and machine operational speed on the field capacity and efficiency of a developed prototype tractor-drawn turmeric planter was studied. This was done with a view to ascertaining how the field capacity and field efficiency were affected by the turmeric rhizome lengths and tractor operational speed. The turmeric rhizome planter consists of trapezoidal hopper, grooved cylindrical metering devise, rectangular frame, ground wheels made of mild steel, furrow opener, chain/sprocket drive system, three linkage point seed delivery tube and press wheel. The experiment was randomized in a factorial design of three levels of rhizome lengths (30, 45 and 60 mm) and operational speeds of 8, 10, and 12 kmh-1. About 3 kg cleaned turmeric rhizomes were introduced into each hopper of the planter and were planted 30 m2 of experimental plot. During the field evaluation of the planter, the effective field capacity, field efficiency, missing index, multiple index and percentage rhizome bruise were evaluated. 30.08% was recorded for maximum percentage bruise on the rhizome. The mean effective field capacity ranged between 0.63 – 0.96hah-1 at operational speeds of 8 and 12kmh-1 respectively and 45 mm rhizome length. The result also shows that the mean efficiency was obtained to be 65.8%. The percentage rhizome bruise decreases with increase in operational speed. The highest and lowest percentage turmeric rhizome miss index of 35% were recorded for turmeric rhizome length of 30 mm at a speed of 10 kmhr-1 and 8 kmhr-1, respectively. The potential implications of the experimental result is to determine the optimal machine process conditions for higher field capacity and gross reduction in mechanical injury (bruise) of planted turmeric rhizomes.

Keywords: rhizome sizes, operational speed, field capacity. field efficiency, turmeric rhizome, planter

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Authors: Ravneet Kaur, Joydeep Acharya, Sudhanshu Gaur

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Artificial Intelligence (AI) has the potential to transform manufacturing by improving shop floor processes such as production, maintenance and quality. However, industrial datasets are notoriously difficult to extract in a real-time, streaming fashion thus, negating potential AI benefits. The main example is some specialized industrial controllers that are operated by custom software which complicates the process of connecting them to an Information Technology (IT) based data acquisition network. Security concerns may also limit direct physical access to these controllers for data acquisition. To connect the Operational Technology (OT) data stored in these controllers to an AI application in a secure, reliable and available way, we propose a novel Industrial IoT (IIoT) solution in this paper. In this solution, we demonstrate how video cameras can be installed in a factory shop floor to continuously obtain images of the controller HMIs. We propose image pre-processing to segment the HMI into regions of streaming data and regions of fixed meta-data. We then evaluate the performance of multiple Optical Character Recognition (OCR) technologies such as Tesseract and Google vision to recognize the streaming data and test it for typical factory HMIs and realistic lighting conditions. Finally, we use the meta-data to match the OCR output with the temporal, domain-dependent context of the data to improve the accuracy of the output. Our IIoT solution enables reliable and efficient data extraction which will improve the performance of subsequent AI applications.

Keywords: human machine interface, industrial internet of things, internet of things, optical character recognition, video analytics

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Authors: A. Mourtzikou, D. Sygkridou, T. Georgakopoulos, G. Katsagounos, E. Stathatos

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Over 60% highly transparent quasi-solid-state dye-sensitized solar cells (DSSCs) with dimension of 50x50 cm² were fabricated via inkjet printing process using nanocomposite inks as raw materials and tested under outdoor illumination conditions. The cells were electrically characterized, and their possible application to the shell of greenhouses was also examined. The panel design was in Z-interconnection, where the working electrode was inkjet printed on one conductive glass and the counter electrode on a second glass in a sandwich configuration. Silver current collective fingers were printed on the glasses to make the internal electrical connections. In that case, the adjacent cells were connected in series via silver fingers and finally insulated using a UV curing resin to protect them from the corrosive (I^-/I₃^-) redox couple of the electrolyte.

Keywords: Dye-sensitized solar panels, inkjet printing, quasi-solid state electrolyte, semi-transparency, scale up

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Authors: Sourour Ramzi, Claudia Sarrico

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Little has been done on network DEA in education, and nobody has attempted to model the whole education supply chain using network DEA. As such the contribution of the present paper is to propose a model for measuring the efficiency of education supply chains using network DEA. First, we use a general survey of data envelopment analysis (DEA) to establish the emergent themes for research in DEA, and focus on the theme of Network DEA. Second, we use a survey on two-stage DEA models, and Network DEA to write a state of the art on Network DEA, particularly applied to supply chain management. Third, we use a survey on DEA applications to establish the most influential papers on DEA education applications, in order to establish the state of the art on applications of DEA in education, in general, and applications of DEA to education using network DEA, in particular. Finally, we propose a model for measuring the performance of education supply chains of different education systems (countries or states within a country, for instance). We then use this model on some empirical data.

Keywords: supply chain, education, data envelopment analysis, network DEA

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Authors: Mohammad Javad Esmaeili

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Background: Coagulopathy is one of the complications with end stage renal disease with high prevalence in the world. Thromboelastogram is adynamic test for evaluation of coagulopathy and we have compared our patient's coagulation profiles with the results of TEG. Material and methods: In this study 50 patients with ESRD who were on regular hemodialysis for at least 6 months was selected with simple sampling and their coagulation profile was done with blood sampling and also TEG was done for every patient. Data were analyzed with SPSS and P<0.05 consider significant. Results: Protein s, Protein c and Antithrombin III deficiency was detected in 32%, 16% and 20% of patients and activated protein c resistance was abnormal in 2% of patients. In TEG, R time in 49% and K in 22/5% of patients was lower than normal and a-angle in 26% and maximum amplitude in 36% of patients was upper than normal (Hypercoagulable state). PS with R and ATIII with K have correlation. Conclusion: R time and K in TEG can be a suitable screening test in patients with suspicious to PS and ATIII deficiency.

Keywords: thromboelastography, chronic kidney disease, Coagulating disorder, hemodialysis

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Authors: Romisaa Ali

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The Policy Gradient approach is one of the deep reinforcement learning families that combines deep neural networks (DNN) with reinforcement learning RL to discover the optimum of the control problem through experience gained from the interaction between the robot and its surroundings. In contrast to earlier policy gradient algorithms, which were unable to handle these two types of error because of over-or under-estimation introduced by the deep neural network model, this article will discuss the state-of-the-art SOTA policy gradient technique, trust region policy optimization (TRPO), by applying this method in various environments compared to another policy gradient method, the Proximal Policy Optimization (PPO), to explain their robust optimization, using this SOTA to gather experience data during various training phases after observing the impact of hyper-parameters on neural network performance.

Keywords: deep neural networks, deep reinforcement learning, proximal policy optimization, state-of-the-art, trust region policy optimization

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Authors: Godiya Pius Atsiya

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One of the most devastating impacts of terrorism on the Nigerian state is the danger it has posed on women, children and other vulnerable groups. The complexity of terrorism in Nigeria, especially in most parts of Northern Nigeria has entrenched unprecedented security challenges such as refugee crisis, kidnapping, food shortages, increase in death tolls, malnutrition, fear, rape and several other psychological factors. Of particular interest in this paper as it relates to terrorism is the high rate of Internally Displaced Persons(IDPs), with women, children and the aged being the most affected. Empirical evidence arising from recent development in Nigeria’s North-East geo-political zone shows that large numbers of refugees fleeing the Boko Haram attacks have doubled. The attendant consequences of this mass exodus of people in the affected areas are that the victims now suffer untold and unwarranted economic hardship. In another dimension, recent findings have it that most powerless women and young teenage girls have been forcefully conscripted into the Islamic extremist groups and used as shields. In some respect, these groups of people have been used as available tools for suicide bombing and other criminal tendencies, the result of which can be detrimental to social cohesion and integration. This work is a theoretical insight into terrorism discourses; hence, the paper relies on existing works of scholars in carrying out the research. The paper argues that the implications of terrorism on women gender have grounding effects on the moral psyche of women who are supposed to be home managers and custodians of morality in society. The burden of terrorism and all it tends to propagate has literally upturned social lives and hence, Nigeria is gradually being plunged into the Hobesian state of nature. As a panacea to resolving this social malaise, the paper submits that government and indeed, all stakeholders in the nation’s democratic project must expedite action to nip this trend in the bud. The paper sums up with conclusion and other alternative policy measures to mitigate the challenges of terrorism in Nigeria.

Keywords: changing democratic state, policy measures, terrorism, women

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Authors: Zsolt Lipcsey, Sampson Marshal Imeh

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We investigate a certain characterisation for rank of a semigroup by Howie and Ribeiro (1999), to ascertain the relevance of the concept of independence. There are cases where the concept of independence fails to be useful for this purpose. One would expect the basic element to be the maximal independent subset of a given semigroup. However, we construct examples for semigroups where finite basis exist and the basis is larger than the number of independent elements.

Keywords: generating sets, independent set, rank, cyclic semigroup, basis, commutative

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Authors: Reza Rezaeipour Honarmandzad

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In this paper we propose an ANN for detection and localization of fatigue damage in aircraft structures. We used network of piezoelectric transducers for Lamb-wave measurements in order to calculate damage indices. Data gathered by the sensors was given to neural network classifier. A set of neural network electors of different architecture cooperates to achieve consensus concerning the state of each monitored path. Sensed signal variations in the ROI, detected by the networks at each path, were used to assess the state of the structure as well as to localize detected damage and to filter out ambient changes. The classifier has been extensively tested on large data sets acquired in the tests of specimens with artificially introduced notches as well as the results of numerous fatigue experiments. Effect of the classifier structure and test data used for training on the results was evaluated.

Keywords: ANN, fatigue damage, aircraft structures, piezoelectric transducers, lamb-wave measurements

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Authors: Adriana Haulica

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Powered by Machine Learning, Precise medicine is tailored by now to use genetic and molecular profiling, with the aim of optimizing the therapeutic benefits for cohorts of patients. As the majority of Machine Language algorithms come from heuristics, the outputs have contextual validity. This is not very restrictive in the sense that medicine itself is not an exact science. Meanwhile, the progress made in Molecular Biology, Bioinformatics, Computational Biology, and Precise Medicine, correlated with the huge amount of human biology data and the increase in computational power, opens new healthcare challenges. A more accurate diagnosis is needed along with real-time treatments by processing as much as possible from the available information. The purpose of this paper is to present a deeper vision for the future of Artificial Intelligence in Precise medicine. In fact, actual Machine Learning algorithms use standard mathematical knowledge, mostly Euclidian metrics and standard computation rules. The loss of information arising from the classical methods prevents obtaining 100% evidence on the diagnosis process. To overcome these problems, we introduce MEDICOMPILLS, a new architectural concept tool of information processing in Precise medicine that delivers diagnosis and therapy advice. This tool processes poly-field digital resources: global knowledge related to biomedicine in a direct or indirect manner but also technical databases, Natural Language Processing algorithms, and strong class optimization functions. As the name suggests, the heart of this tool is a compiler. The approach is completely new, tailored for omics and clinical data. Firstly, the intrinsic biological intuition is different from the well-known “a needle in a haystack” approach usually used when Machine Learning algorithms have to process differential genomic or molecular data to find biomarkers. Also, even if the input is seized from various types of data, the working engine inside the MEDICOMPILLS does not search for patterns as an integrative tool. This approach deciphers the biological meaning of input data up to the metabolic and physiologic mechanisms, based on a compiler with grammars issued from bio-algebra-inspired mathematics. It translates input data into bio-semantic units with the help of contextual information iteratively until Bio-Logical operations can be performed on the base of the “common denominator “rule. The rigorousness of MEDICOMPILLS comes from the structure of the contextual information on functions, built to be analogous to mathematical “proofs”. The major impact of this architecture is expressed by the high accuracy of the diagnosis. Detected as a multiple conditions diagnostic, constituted by some main diseases along with unhealthy biological states, this format is highly suitable for therapy proposal and disease prevention. The use of MEDICOMPILLS architecture is highly beneficial for the healthcare industry. The expectation is to generate a strategic trend in Precise medicine, making medicine more like an exact science and reducing the considerable risk of errors in diagnostics and therapies. The tool can be used by pharmaceutical laboratories for the discovery of new cures. It will also contribute to better design of clinical trials and speed them up.

Keywords: bio-semantic units, multiple conditions diagnosis, NLP, omics

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Authors: Maryam Moosavi, Hadi Khatibzadeh

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The main purpose of this paper is to study the proximal point algorithm for quasi-nonexpansive mappings in Hadamard spaces. △-convergence and strong convergence of cyclic resolvents for a finite family of quasi-nonexpansive mappings one to a fixed point of the mappings are established

Keywords: Fixed point, Hadamard space, Proximal point algorithm, Quasi-nonexpansive sequence of mappings, Resolvent

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Authors: Adam J. Bowen

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Abstract— Should robots have rights or legal protections. Often debates concerning whether robots and AI should be afforded rights focus on conditions of personhood and the possibility of future advanced forms of AI satisfying particular intrinsic cognitive and moral attributes of rights-holding persons. Such discussions raise compelling questions about machine consciousness, autonomy, and value alignment with human interests. Although these are important theoretical concerns, especially from a future design perspective, they provide limited guidance for addressing the moral and legal standing of current and near-term AI that operate well below the cognitive and moral agency of human persons. Robots and AI are already being pressed into service in a wide range of roles, especially in healthcare and biomedical contexts. The design and large-scale implementation of robots in the context of core societal institutions like healthcare systems continues to rapidly develop. For example, we bring them into our homes, hospitals, and other care facilities to assist in care for the sick, disabled, elderly, children, or otherwise vulnerable persons. We enlist surgical robotic systems in precision tasks, albeit still human-in-the-loop technology controlled by surgeons. We also entrust them with social roles involving companionship and even assisting in intimate caregiving tasks (e.g., bathing, feeding, turning, medicine administration, monitoring, transporting). There have been advances to enable severely disabled persons to use robots to feed themselves or pilot robot avatars to work in service industries. As the applications for near-term AI increase and the roles of robots in restructuring our biomedical practices expand, we face pressing questions about the normative implications of human-robot interactions and collaborations in our collective worldmaking, as well as the moral and legal status of robots. This paper argues that robots operating in public and private spaces be afforded some protections as either moral patients or legal agents to establish prohibitions on robot abuse, misuse, and mistreatment. We already implement robots and embed them in our practices and institutions, which generates a host of human-to-machine and machine-to-machine relationships. As we interact with machines, whether in service contexts, medical assistance, or home health companions, these robots are first encountered in relationship to us and our respective roles in the encounter (e.g., surgeon, physical or occupational therapist, recipient of care, patient’s family, healthcare professional, stakeholder). This proposal aims to outline a framework for establishing limiting factors and determining the extent of moral or legal protections for robots. In doing so, it advocates for a relational approach that emphasizes the priority of mapping the complex contextually sensitive roles played and the relations in which humans and robots stand to guide policy determinations by relevant institutions and authorities. The relational approach must also be technically informed by the intended uses of the biomedical technologies in question, Design History Files, extensive risk assessments and hazard analyses, as well as use case social impact assessments.

Keywords: biomedical robots, robot ethics, robot laws, human-robot interaction

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Authors: Lizhu Y. Davis, Gretchen Higginbottom, Vang Vang

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This presentation explores the adoption of library resources to engage students in a Visual Merchandising course during the 2016 spring semester. This study was a cross-disciplinary collaboration between the Fashion Merchandising Program and the Madden Library at California State University, Fresno. The goal of the project was to explore and assess the students’ use of library resources as a part of the Affordable Learning Solutions Initiative, a California State University (CSU) Office of the Chancellor Program that enables faculty to choose and provide high-quality, free or low-cost educational materials for their students. Students were interviewed afterwards and the results were generally favorable and provided insight into how students perceive and use library resources to support their research needs. This study reveals an important step in examining how open educational resources impact student learning.

Keywords: collaboration, library resources, open educational resources, visual merchandising

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Authors: Zeynep Selin Acar

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In international system, there has always been a cycle of violence, war and peace. As there travels the time, after Christianity and later Just War theorists, international relations theorists have been tried to limit violence and war. As pieces of international law, Peace of Augsburg, Kellog-Briand Pact, League of Nations Covenant and UN Charter were and are still not effective to prevent war. Moreover, in order to find a way around these rules, it is believed that a new excuse started to be used instead of violence or war, humanitarian intervention. However, it has neither a legal nor a universally accepted framework. As a result, it is open to be manipulated by states. In order to prevent this, Responsibility to Protect (RtoP) which gives a state the responsibility to protect its citizens against violence, is created. Additionally, RtoP transfers this responsibility to regional or international group of states at the time when a state is the origin of violence. In the lights of these, this paper analyzes RtoP as an ethical approach to war and peace studies because it provides other states as guardians and care-takers of people who do not belong to them or do not share any togetherness.

Keywords: ethics, humanitarian intervention, responsibility to protect, UN charter

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